To quantitatively examine the efficacy of vegetation restoration in drylands globally.
#general library loads
library(tidyverse)
#functions
se <- function(x){
sd(x)/sqrt(length(x))
}
#study data####
studies <- read_csv("data/studies.csv")
#studies included
evidence <- studies %>%
filter(exclude == "no")
ggplot(evidence, aes(disturbance, fill = paradigm)) +
geom_bar(na.rm = TRUE) +
coord_flip() +
scale_fill_brewer(palette = "Paired") +
labs(y = "frequency")
ggplot(evidence, aes(intervention, fill = paradigm)) +
geom_bar(na.rm = TRUE) +
coord_flip() +
scale_fill_brewer(palette = "Paired") +
labs(y = "frequency")
#paradigm
derived.evidence <- evidence %>%
group_by(technique, data, region, disturbance, goal, paradigm) %>% summarise(n = n())
#active-passive split
m <- glm(n~paradigm, family = poisson, derived.evidence)
anova(m, test="Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: n
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 167 9.9147
## paradigm 1 0.045115 166 9.8696 0.8318
#region
m1 <- glm(n~paradigm*region, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m1, test="Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: n
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 167 9.9147
## paradigm 1 0.045115 166 9.8696 0.8318
## region 6 0.301367 160 9.5682 0.9995
## paradigm:region 6 0.213627 154 9.3546 0.9998
#outcome
m2 <- glm(n~paradigm*goal, family = poisson, derived.evidence)
#m1
#summary(m1)
anova(m2, test="Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: n
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 167 9.9147
## paradigm 1 0.045115 166 9.8696 0.8318
## goal 6 0.240941 160 9.6287 0.9997
## paradigm:goal 4 0.301480 156 9.3272 0.9897
library(PRISMAstatement)
prisma(found = 1504,
found_other = 5,
no_dupes = 1039,
screened = 1039,
screen_exclusions = 861,
full_text = 178,
full_text_exclusions = 101,
qualitative = 77,
quantitative = 66,
width = 500, height = 500)
#all data includes non-relevant and inc search term studies
data_all <- read_csv("data/data_all.csv")
#data from ag & grazing studies that examined restoration in drylands
data <- data_all %>%
filter(disturbance %in% c("agriculture","grazing")) %>%
filter(!notes %in% "couldnt extract data") %>%
mutate(lrr = log(mean.t/mean.c), var.es = ((sd.t^2/(n.t*mean.t^2)) + (sd.c^2/(n.c*mean.c^2)))) %>%
filter(!is.na(lrr)) %>%
filter(!is.na(var.es)) %>%
filter(!is.na(n.t)) %>%
filter(!is.na(p)) %>%
filter(!is.na(intervention)) %>%
filter(is.finite(lrr)) %>%
filter(!is.na(exp.length)) %>%
filter(!is.na(MAP)) %>%
filter(!is.na(aridity.index))
#write cleaned data for provenace and more rapid reuse
#write_csv(data, "data/data.csv")
#evidence map####
require(maps)
world<-map_data("world")
map<-ggplot() + geom_polygon(data=world, fill="gray50", aes(x=long, y=lat, group=group))
map + geom_point(data=data, aes(x=long, y=lat, color = paradigm)) +
scale_color_brewer(palette = "Paired") +
labs(x = "longitude", y = "latitude", color = "")
#meta####
library(meta)
#active-passive differences####
m1 <- metagen(lrr, var.es, studlab = ID, comb.fixed = FALSE, byvar = paradigm, data = data)
summary(m1)
## Number of studies combined: k = 1460
##
## 95%-CI z p-value
## Random effects model 0.0766 [0.0654; 0.0879] 13.38 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.0450; H = 184527270028.74 [184527270027.80; 184527270029.68]; I^2 = 100.0% [100.0%; 100.0%]
##
## Test of heterogeneity:
## Q d.f. p-value
## 49679407227636228414242816.00 1459 0
##
## Results for subgroups (random effects model):
## k 95%-CI
## paradigm = active 1102 0.2184 [ 0.2055; 0.2314]
## paradigm = passive 358 -0.3413 [-0.3753; -0.3073]
## Q tau^2 I^2
## paradigm = active 49671693556322550581035008.00 0.0450 100.0%
## paradigm = passive 4152232320954931871744.00 0.1047 100.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 911.23 1 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
#funnel(m1)
#radial(m1)
#t-tests if different from 0
tmu <- function(x){t.test(x, mu = 0, paired = FALSE, var.equal=FALSE, conf.level = 0.95)
}
data %>%
split(.$paradigm) %>%
purrr::map(~tmu(.$lrr)) #note this uses arithmetic means not estimated means from random effect models
## $active
##
## One Sample t-test
##
## data: x
## t = 7.6083, df = 1101, p-value = 5.943e-14
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.2069479 0.3507825
## sample estimates:
## mean of x
## 0.2788652
##
##
## $passive
##
## One Sample t-test
##
## data: x
## t = -7.5438, df = 357, p-value = 3.824e-13
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.4408271 -0.2585133
## sample estimates:
## mean of x
## -0.3496702
#metareg(m1, ~aridity.index+exp.length) #covariates and additive
mr1 <- metareg(m1, ~aridity.index*exp.length)#interaction term
mr1
##
## Mixed-Effects Model (k = 1460; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0440 (SE = 0.0167)
## tau (square root of estimated tau^2 value): 0.2098
## I^2 (residual heterogeneity / unaccounted variability): 100.00%
## H^2 (unaccounted variability / sampling variability): 33306623692228221992960.00
## R^2 (amount of heterogeneity accounted for): 2.22%
##
## Test for Residual Heterogeneity:
## QE(df = 1456) = 48494444095884294040322048.0000, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5927.5166, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.4843 0.0171 28.3215 <.0001 0.4508
## aridity.index -0.0142 0.0008 -18.3338 <.0001 -0.0158
## exp.length 0.0025 0.0001 35.5749 <.0001 0.0024
## aridity.index:exp.length -0.0002 0.0000 -48.9045 <.0001 -0.0002
## ci.ub
## intrcpt 0.5178 ***
## aridity.index -0.0127 ***
## exp.length 0.0027 ***
## aridity.index:exp.length -0.0001 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mr1)
#interventions####
#active
m2 <- metagen(lrr, var.es, studlab = ID, byvar = intervention, comb.fixed=FALSE, subset = paradigm == "active", data = data)
summary(m2)
## Number of studies combined: k = 1102
##
## 95%-CI z p-value
## Random effects model 0.2184 [0.2055; 0.2314] 33.02 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.0450; H = 212403086959.62 [212403086958.53; 212403086960.71]; I^2 = 100.0% [100.0%; 100.0%]
##
## Test of heterogeneity:
## Q d.f. p-value
## 49671693556322550581035008.00 1101 0
##
## Results for subgroups (random effects model):
## k 95%-CI
## intervention = vegetation 779 0.1845 [0.1694; 0.1996]
## intervention = soil 248 0.3128 [0.2990; 0.3265]
## intervention = water addition 75 0.6409 [0.5539; 0.7279]
## Q tau^2 I^2
## intervention = vegetation 48393423300974332080029696.00 0.0443 100.0%
## intervention = soil 97513761686148203151360.00 0.0111 100.0%
## intervention = water addition 31132.18 0.1047 99.8%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 226.58 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
#funnel(m2)
#radial(m2)
#metabias(m2)
#metareg(m2, ~ aridity.index + exp.length)
mr2 <- metareg(m2, ~ aridity.index*exp.length)
plot(mr2)
#passive
m3 <- metagen(lrr, var.es, studlab = ID, byvar = intervention, subset = paradigm == "passive", comb.fixed=FALSE, data = data)
summary(m3)
## Number of studies combined: k = 358
##
## 95%-CI z p-value
## Random effects model -0.3413 [-0.3753; -0.3073] -19.70 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.1047; H = 3410410951.75 [3410410950.14; 3410410953.36]; I^2 = 100.0% [100.0%; 100.0%]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4152232320954931871744.00 357 0
##
## Results for subgroups (random effects model):
## k 95%-CI
## intervention = vegetation 125 0.2654 [ 0.2067; 0.3241]
## intervention = grazing exclusion 29 0.1351 [ 0.0270; 0.2431]
## intervention = soil 204 -0.7583 [-0.8196; -0.6970]
## Q tau^2 I^2
## intervention = vegetation 4152209524073903423488.00 0.1047 100.0%
## intervention = grazing exclusion 238316232.18 0.0881 100.0%
## intervention = soil 14453104123321616.00 0.1990 100.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 595.91 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
#funnel(m3)
#radial(m3)
#metabias(m3, method = "linreg")
#metareg(m3, ~ aridity.index + exp.length)
mr3 <- metareg(m3, ~ aridity.index*exp.length)
mr3
##
## Mixed-Effects Model (k = 358; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1047 (SE = 0.0876)
## tau (square root of estimated tau^2 value): 0.3236
## I^2 (residual heterogeneity / unaccounted variability): 100.00%
## H^2 (unaccounted variability / sampling variability): 11729120705162215424.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 354) = 4152108729627424325632.0000, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 882.1252, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1386 0.0636 2.1770 0.0295 0.0138
## aridity.index 0.0042 0.0029 1.4244 0.1543 -0.0016
## exp.length 0.0106 0.0015 6.8767 <.0001 0.0076
## aridity.index:exp.length -0.0005 0.0001 -8.1677 <.0001 -0.0006
## ci.ub
## intrcpt 0.2633 *
## aridity.index 0.0099
## exp.length 0.0136 ***
## aridity.index:exp.length -0.0004 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mr3)
#outcomes
#active
m4 <- metagen(lrr, var.es, studlab = ID, byvar = outcome, subset = paradigm == "active", comb.fixed=FALSE, data = data)
summary(m4)
## Number of studies combined: k = 1102
##
## 95%-CI z p-value
## Random effects model 0.2184 [0.2055; 0.2314] 33.02 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.0450; H = 212403086959.62 [212403086958.53; 212403086960.71]; I^2 = 100.0% [100.0%; 100.0%]
##
## Test of heterogeneity:
## Q d.f. p-value
## 49671693556322550581035008.00 1101 0
##
## Results for subgroups (random effects model):
## k 95%-CI
## outcome = soil 249 0.2204 [ 0.1558; 0.2849]
## outcome = plants 305 0.5071 [ 0.4936; 0.5206]
## outcome = animals 24 -0.1152 [-0.1155; -0.1148]
## outcome = habitat 524 0.0621 [ 0.0437; 0.0804]
## Q tau^2 I^2
## outcome = soil 35077220764051.67 0.2656 100.0%
## outcome = plants 97513760543782884868096.00 0.0111 100.0%
## outcome = animals 541696.64 <0.0001 100.0%
## outcome = habitat 48393423303339003634253824.00 0.0443 100.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 8647.81 3 0
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
#metabias(m)
mr4 <- metareg(m4, ~aridity.index*exp.length)
mr4
##
## Mixed-Effects Model (k = 1102; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0440 (SE = 0.0167)
## tau (square root of estimated tau^2 value): 0.2098
## I^2 (residual heterogeneity / unaccounted variability): 100.00%
## H^2 (unaccounted variability / sampling variability): 44155839198290107170816.00
## R^2 (amount of heterogeneity accounted for): 2.23%
##
## Test for Residual Heterogeneity:
## QE(df = 1098) = 48483111439722536499150848.0000, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2277.4091, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5600 0.0193 29.0873 <.0001 0.5222
## aridity.index -0.0145 0.0009 -15.8801 <.0001 -0.0163
## exp.length 0.0033 0.0001 26.6964 <.0001 0.0030
## aridity.index:exp.length -0.0002 0.0000 -20.9978 <.0001 -0.0003
## ci.ub
## intrcpt 0.5977 ***
## aridity.index -0.0127 ***
## exp.length 0.0035 ***
## aridity.index:exp.length -0.0002 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mr4)
#passive
m5 <- metagen(lrr, var.es, studlab = ID, byvar = outcome, subset = paradigm == "passive", comb.fixed=FALSE, data = data)
m5
## 95%-CI %W(random) outcome
## 66.1 0.1577 [ 0.0712; 0.2441] 0.3 habitat
## 66.1 0.1039 [ 0.0334; 0.1743] 0.3 habitat
## 66.1 0.1005 [-0.0464; 0.2474] 0.3 habitat
## 66.1 -0.1054 [-0.2277; 0.0170] 0.3 habitat
## 66.1 0.2877 [ 0.1365; 0.4389] 0.3 habitat
## 66.1 0.6931 [ 0.5722; 0.8141] 0.3 habitat
## 66.1 0.2877 [ 0.1365; 0.4389] 0.3 habitat
## 66.1 0.6931 [ 0.4209; 0.9654] 0.2 habitat
## 66.1 0.3007 [ 0.2574; 0.3439] 0.3 habitat
## 66.1 0.2314 [ 0.1922; 0.2706] 0.3 habitat
## 66.1 0.1456 [ 0.1231; 0.1681] 0.3 habitat
## 66.1 0.0800 [ 0.0586; 0.1015] 0.3 habitat
## 66.2 0.3389 [ 0.1930; 0.4847] 0.3 habitat
## 66.2 -0.1355 [-0.2580; -0.0131] 0.3 habitat
## 66.2 -0.1606 [-0.3374; 0.0161] 0.3 habitat
## 66.2 0.2703 [ 0.1227; 0.4179] 0.3 habitat
## 66.2 0.2231 [ 0.0222; 0.4240] 0.3 habitat
## 66.2 0.0000 [-0.4355; 0.4355] 0.2 habitat
## 66.2 0.0000 [-0.2450; 0.2450] 0.2 habitat
## 66.2 -0.6931 [-0.9654; -0.4209] 0.2 habitat
## 66.2 0.3084 [ 0.2650; 0.3517] 0.3 habitat
## 66.2 0.1523 [ 0.1185; 0.1861] 0.3 habitat
## 66.2 0.3261 [ 0.3069; 0.3453] 0.3 habitat
## 66.2 0.0963 [ 0.0818; 0.1108] 0.3 habitat
## 69.1 2.9202 [ 2.4502; 3.3902] 0.2 plants
## 69.1 2.8116 [ 2.3655; 3.2577] 0.2 plants
## 69.2 0.6061 [ 0.3912; 0.8211] 0.3 plants
## 69.2 0.3773 [ 0.1223; 0.6323] 0.2 plants
## 69.3 1.3228 [ 1.2066; 1.4391] 0.3 plants
## 69.3 1.0071 [ 0.8919; 1.1223] 0.3 plants
## 69.4 1.2580 [ 1.1504; 1.3656] 0.3 plants
## 69.4 0.9428 [ 0.8361; 1.0494] 0.3 plants
## 69.5 0.6840 [ 0.6558; 0.7123] 0.3 plants
## 69.5 0.8129 [ 0.7902; 0.8357] 0.3 plants
## 115.1 -0.2531 [-0.2841; -0.2221] 0.3 plants
## 115.1 -0.0672 [-0.0689; -0.0656] 0.3 plants
## 115.1 -0.0544 [-0.0568; -0.0519] 0.3 plants
## 115.2 -0.0870 [-0.0871; -0.0869] 0.3 plants
## 115.2 0.0000 [-0.0001; 0.0001] 0.3 plants
## 115.2 0.0000 [-0.0001; 0.0001] 0.3 plants
## 115.3 0.4055 [ 0.4054; 0.4055] 0.3 plants
## 115.3 0.2877 [ 0.2819; 0.2934] 0.3 plants
## 115.3 0.4329 [ 0.4309; 0.4348] 0.3 plants
## 115.4 -0.0241 [-0.0318; -0.0164] 0.3 plants
## 115.4 -0.1021 [-0.1086; -0.0957] 0.3 plants
## 115.4 -0.0373 [-0.0437; -0.0309] 0.3 plants
## 115.5 0.0707 [ 0.0705; 0.0710] 0.3 plants
## 115.5 -0.0130 [-0.0133; -0.0127] 0.3 plants
## 115.5 0.0128 [ 0.0111; 0.0146] 0.3 plants
## 115.6 0.4683 [ 0.4674; 0.4693] 0.3 plants
## 115.6 0.3897 [ 0.3877; 0.3916] 0.3 plants
## 115.6 0.4891 [ 0.4875; 0.4907] 0.3 plants
## 115.7 -0.0227 [-0.0382; -0.0073] 0.3 plants
## 115.7 -0.0618 [-0.0800; -0.0436] 0.3 plants
## 115.7 0.0259 [ 0.0100; 0.0417] 0.3 plants
## 135.1 -0.0606 [-0.1516; 0.0303] 0.3 habitat
## 135.1 0.0572 [-0.0686; 0.1829] 0.3 habitat
## 135.1 0.1112 [-0.0084; 0.2309] 0.3 habitat
## 135.101 0.9808 [ 0.9803; 0.9813] 0.3 habitat
## 135.101 0.6931 [ 0.6492; 0.7371] 0.3 habitat
## 135.101 0.2877 [ 0.2870; 0.2884] 0.3 habitat
## 135.2 -0.0807 [-0.1665; 0.0052] 0.3 habitat
## 135.2 0.0380 [-0.0477; 0.1238] 0.3 habitat
## 135.2 0.1169 [ 0.0306; 0.2032] 0.3 habitat
## 135.3 0.3570 [ 0.2700; 0.4440] 0.3 habitat
## 135.3 0.1732 [ 0.0863; 0.2602] 0.3 habitat
## 135.3 -0.1346 [-0.2217; -0.0475] 0.3 habitat
## 135.4 -0.0359 [-0.1232; 0.0513] 0.3 habitat
## 135.4 0.0278 [-0.0594; 0.1151] 0.3 habitat
## 135.4 0.1036 [ 0.0165; 0.1907] 0.3 habitat
## 135.5 0.2689 [ 0.1818; 0.3560] 0.3 habitat
## 135.5 0.7015 [ 0.6144; 0.7887] 0.3 habitat
## 135.5 0.4324 [ 0.3453; 0.5195] 0.3 habitat
## 135.6 0.8330 [ 0.7462; 0.9198] 0.3 habitat
## 135.6 0.1542 [ 0.1110; 0.1973] 0.3 habitat
## 135.6 0.5133 [ 0.4264; 0.6002] 0.3 habitat
## 135.7 -0.1978 [-0.2850; -0.1107] 0.3 habitat
## 135.7 -0.1490 [-0.2360; -0.0619] 0.3 habitat
## 135.7 -0.1724 [-0.2596; -0.0852] 0.3 habitat
## 135.8 0.0408 [-0.0463; 0.1278] 0.3 habitat
## 135.8 0.3003 [ 0.2133; 0.3874] 0.3 habitat
## 135.8 0.1499 [ 0.0628; 0.2370] 0.3 habitat
## 135.9 0.2926 [ 0.2056; 0.3796] 0.3 habitat
## 135.9 0.3061 [ 0.2196; 0.3926] 0.3 habitat
## 135.9 0.4060 [ 0.3193; 0.4926] 0.3 habitat
## 170.1 -0.1592 [-0.2137; -0.1047] 0.3 plants
## 170.2 -0.2465 [-0.2728; -0.2203] 0.3 plants
## 170.3 -0.3137 [-0.3425; -0.2849] 0.3 plants
## 207.1 0.1511 [ 0.0567; 0.2455] 0.3 habitat
## 207.2 -0.2283 [-0.5724; 0.1159] 0.2 habitat
## 207.3 -0.2877 [-0.5032; -0.0722] 0.3 habitat
## 207.4 1.3328 [ 1.3136; 1.3520] 0.3 habitat
## 207.5 0.7504 [ 0.7339; 0.7670] 0.3 habitat
## 207.6 0.3419 [ 0.2044; 0.4793] 0.3 habitat
## 207.7 1.1732 [ 1.1038; 1.2425] 0.3 habitat
## 207.8 1.6834 [ 1.6461; 1.7207] 0.3 habitat
## 207.9 -0.0431 [-0.0435; -0.0426] 0.3 habitat
## 207.101 -0.7791 [-0.7851; -0.7730] 0.3 habitat
## 207.11 -0.1417 [-0.1433; -0.1400] 0.3 habitat
## 207.12 -0.1621 [-0.1699; -0.1543] 0.3 habitat
## 207.13 0.1369 [ 0.1347; 0.1391] 0.3 habitat
## 207.14 0.0000 [-0.0353; 0.0353] 0.3 habitat
## 207.15 0.1825 [ 0.1761; 0.1890] 0.3 habitat
## 223.1 -1.6391 [-3.6281; 0.3499] 0.0 habitat
## 223.3 -2.2399 [-3.7867; -0.6931] 0.0 habitat
## 223.4 -3.1272 [-5.0453; -1.2091] 0.0 habitat
## 223.1 -2.2843 [-2.3452; -2.2233] 0.3 habitat
## 223.2 -2.4170 [-4.3963; -0.4376] 0.0 habitat
## 223.3 -1.0860 [-1.1974; -0.9746] 0.3 habitat
## 223.4 -1.8779 [-2.0405; -1.7152] 0.3 habitat
## 223.1 0.2431 [ 0.2068; 0.2793] 0.3 habitat
## 223.2 0.3555 [ 0.2462; 0.4649] 0.3 habitat
## 223.3 0.2036 [ 0.1341; 0.2731] 0.3 habitat
## 223.4 0.5882 [ 0.5371; 0.6393] 0.3 habitat
## 223.1 -0.1316 [-0.1347; -0.1286] 0.3 habitat
## 223.2 -0.1694 [-0.1711; -0.1676] 0.3 habitat
## 223.3 -0.0671 [-0.0699; -0.0643] 0.3 habitat
## 223.4 -0.1316 [-0.1351; -0.1282] 0.3 habitat
## 231.1 0.8560 [ 0.8325; 0.8796] 0.3 plants
## 231.2 -0.5733 [-0.5786; -0.5681] 0.3 plants
## 231.3 0.7340 [ 0.7110; 0.7570] 0.3 plants
## 231.4 0.7710 [ 0.7299; 0.8121] 0.3 plants
## 231.5 0.5077 [-1.6298; 2.6453] 0.0 plants
## 231.6 0.3689 [ 0.2673; 0.4705] 0.3 plants
## 231.7 -0.0635 [-0.0937; -0.0333] 0.3 plants
## 239.1 0.2595 [ 0.1996; 0.3194] 0.3 plants
## 239.2 1.3863 [ 1.3863; 1.3863] 0.3 plants
## 239.3 -0.6931 [-0.6931; -0.6931] 0.3 plants
## 239.4 2.1513 [ 1.8069; 2.4957] 0.2 plants
## 247.1 0.7276 [ 0.7196; 0.7356] 0.3 soil
## 247.1 0.7772 [ 0.7693; 0.7851] 0.3 soil
## 247.1 0.7361 [ 0.7281; 0.7440] 0.3 soil
## 247.1 0.7191 [ 0.7118; 0.7264] 0.3 soil
## 247.1 0.7105 [ 0.7029; 0.7182] 0.3 soil
## 247.1 0.6574 [ 0.6458; 0.6690] 0.3 soil
## 247.1 0.6391 [ 0.6262; 0.6519] 0.3 soil
## 247.1 0.6013 [ 0.5939; 0.6088] 0.3 soil
## 247.1 0.5002 [ 0.4918; 0.5087] 0.3 soil
## 247.1 0.5419 [ 0.5344; 0.5494] 0.3 soil
## 247.1 0.4895 [ 0.4796; 0.4995] 0.3 soil
## 247.101 -1.6851 [-1.6865; -1.6837] 0.3 soil
## 247.101 -1.6851 [-1.7034; -1.6669] 0.3 soil
## 247.101 -1.8819 [-1.8984; -1.8655] 0.3 soil
## 247.101 -1.9264 [-1.9551; -1.8977] 0.3 soil
## 247.101 -1.5207 [-1.5820; -1.4595] 0.3 soil
## 247.101 -1.6851 [-1.6884; -1.6819] 0.3 soil
## 247.101 -1.6851 [-1.6865; -1.6837] 0.3 soil
## 247.101 -2.0218 [-2.0641; -1.9795] 0.3 soil
## 247.101 -1.8394 [-1.8453; -1.8334] 0.3 soil
## 247.101 -1.1888 [-1.1902; -1.1874] 0.3 soil
## 247.101 -0.7979 [-0.8084; -0.7875] 0.3 soil
## 247.11 -1.0981 [-1.1031; -1.0932] 0.3 soil
## 247.11 -1.5945 [-1.5979; -1.5912] 0.3 soil
## 247.11 -1.9345 [-1.9608; -1.9082] 0.3 soil
## 247.11 -2.0364 [-2.0769; -1.9959] 0.3 soil
## 247.11 -1.3432 [-1.3755; -1.3110] 0.3 soil
## 247.11 -1.5945 [-1.5979; -1.5912] 0.3 soil
## 247.11 -1.1426 [-1.1433; -1.1418] 0.3 soil
## 247.11 -1.2641 [-1.2658; -1.2624] 0.3 soil
## 247.11 -1.1209 [-1.1209; -1.1209] 0.3 soil
## 247.11 -0.9755 [-0.9784; -0.9726] 0.3 soil
## 247.11 -0.6646 [-0.6651; -0.6640] 0.3 soil
## 247.12 -1.0272 [-1.1433; -0.9110] 0.3 soil
## 247.12 -1.3264 [-1.4949; -1.1579] 0.3 soil
## 247.12 -1.7554 [-1.8868; -1.6240] 0.3 soil
## 247.12 -1.6536 [-1.7562; -1.5511] 0.3 soil
## 247.12 -1.9966 [-2.0962; -1.8969] 0.3 soil
## 247.12 -2.1972 [-2.2934; -2.1010] 0.3 soil
## 247.12 -1.6536 [-1.8139; -1.4933] 0.3 soil
## 247.12 -1.6864 [-1.7710; -1.6018] 0.3 soil
## 247.12 -1.2809 [-1.3642; -1.1977] 0.3 soil
## 247.12 -0.9287 [-1.0074; -0.8500] 0.3 soil
## 247.12 -0.7181 [-0.7972; -0.6391] 0.3 soil
## 247.13 -1.6619 [-1.7104; -1.6134] 0.3 soil
## 247.13 -1.7630 [-1.7660; -1.7600] 0.3 soil
## 247.13 -1.1883 [-1.1947; -1.1819] 0.3 soil
## 247.13 -1.3227 [-1.3269; -1.3185] 0.3 soil
## 247.13 -1.2564 [-1.2663; -1.2466] 0.3 soil
## 247.13 -1.3936 [-1.4027; -1.3845] 0.3 soil
## 247.13 -1.0968 [-1.0983; -1.0953] 0.3 soil
## 247.13 -1.3227 [-1.3238; -1.3215] 0.3 soil
## 247.13 -1.0436 [-1.0504; -1.0368] 0.3 soil
## 247.13 -0.9172 [-0.9244; -0.9100] 0.3 soil
## 247.13 -0.7042 [-0.7113; -0.6970] 0.3 soil
## 247.14 0.1027 [ 0.0869; 0.1185] 0.3 soil
## 247.14 -0.2209 [-0.2241; -0.2177] 0.3 soil
## 247.14 -0.1348 [-0.1375; -0.1321] 0.3 soil
## 247.14 -0.2097 [-0.2119; -0.2075] 0.3 soil
## 247.14 -0.3028 [-0.3055; -0.3002] 0.3 soil
## 247.14 -0.2209 [-0.2218; -0.2199] 0.3 soil
## 247.14 -0.2907 [-0.2924; -0.2890] 0.3 soil
## 247.14 -0.1452 [-0.1499; -0.1404] 0.3 soil
## 247.14 -0.0944 [-0.0987; -0.0901] 0.3 soil
## 247.14 -0.0274 [-0.0285; -0.0263] 0.3 soil
## 247.14 0.0267 [ 0.0260; 0.0273] 0.3 soil
## 247.15 0.7252 [ 0.7243; 0.7262] 0.3 soil
## 247.15 0.6373 [ 0.6367; 0.6378] 0.3 soil
## 247.15 0.5450 [ 0.5430; 0.5470] 0.3 soil
## 247.15 0.6675 [ 0.6669; 0.6680] 0.3 soil
## 247.15 0.5575 [ 0.5572; 0.5579] 0.3 soil
## 247.15 0.5575 [ 0.5563; 0.5588] 0.3 soil
## 247.15 0.2601 [ 0.2582; 0.2621] 0.3 soil
## 247.15 0.3711 [ 0.3707; 0.3714] 0.3 soil
## 247.15 0.3457 [ 0.3454; 0.3461] 0.3 soil
## 247.15 0.2931 [ 0.2923; 0.2939] 0.3 soil
## 247.15 0.2318 [ 0.2307; 0.2329] 0.3 soil
## 247.16 -1.7864 [-1.7979; -1.7748] 0.3 soil
## 247.16 -1.0307 [-1.0338; -1.0276] 0.3 soil
## 247.16 -0.8259 [-0.8267; -0.8251] 0.3 soil
## 247.16 -0.8136 [-0.8168; -0.8105] 0.3 soil
## 247.16 -0.7430 [-0.7458; -0.7402] 0.3 soil
## 247.16 -0.6665 [-0.6690; -0.6640] 0.3 soil
## 247.16 -0.6665 [-0.6690; -0.6640] 0.3 soil
## 247.16 -0.8136 [-0.8168; -0.8105] 0.3 soil
## 247.16 -0.6665 [-0.6678; -0.6651] 0.3 soil
## 247.16 -0.5382 [-0.5403; -0.5361] 0.3 soil
## 247.16 -0.4412 [-0.4421; -0.4404] 0.3 soil
## 247.17 -1.0282 [-1.0335; -1.0229] 0.3 soil
## 247.17 -0.9816 [-0.9825; -0.9808] 0.3 soil
## 247.17 -0.8887 [-0.8895; -0.8879] 0.3 soil
## 247.17 -1.0282 [-1.0298; -1.0265] 0.3 soil
## 247.17 -0.8312 [-0.8441; -0.8183] 0.3 soil
## 247.17 -1.1666 [-1.1716; -1.1615] 0.3 soil
## 247.17 -1.1059 [-1.1064; -1.1054] 0.3 soil
## 247.17 -0.6956 [-0.6965; -0.6946] 0.3 soil
## 247.17 -0.7303 [-0.7305; -0.7301] 0.3 soil
## 247.17 -0.6250 [-0.6317; -0.6182] 0.3 soil
## 247.17 -0.4579 [-0.4606; -0.4552] 0.3 soil
## 247.18 -0.7429 [-0.7442; -0.7416] 0.3 soil
## 247.18 -0.6274 [-0.6311; -0.6237] 0.3 soil
## 247.18 -0.4758 [-0.4767; -0.4750] 0.3 soil
## 247.18 -0.6274 [-0.6330; -0.6218] 0.3 soil
## 247.18 -0.3720 [-0.3735; -0.3706] 0.3 soil
## 247.18 -0.5076 [-0.5085; -0.5067] 0.3 soil
## 247.18 -0.4006 [-0.4014; -0.3998] 0.3 soil
## 247.18 -0.5572 [-0.5604; -0.5539] 0.3 soil
## 247.18 -0.3172 [-0.3180; -0.3165] 0.3 soil
## 247.18 -0.2780 [-0.2835; -0.2725] 0.3 soil
## 247.18 -0.2039 [-0.2043; -0.2036] 0.3 soil
## 247.2 -2.0902 [-2.0903; -2.0902] 0.3 soil
## 247.2 -2.1553 [-2.1553; -2.1552] 0.3 soil
## 247.2 -1.8315 [-1.8335; -1.8295] 0.3 soil
## 247.2 -2.0144 [-2.0208; -2.0080] 0.3 soil
## 247.2 -1.5849 [-1.5862; -1.5837] 0.3 soil
## 247.2 -1.8315 [-1.8359; -1.8270] 0.3 soil
## 247.2 -1.6769 [-1.6784; -1.6754] 0.3 soil
## 247.2 -1.8878 [-1.8900; -1.8855] 0.3 soil
## 247.2 -1.4250 [-1.4251; -1.4250] 0.3 soil
## 247.2 -1.2240 [-1.2246; -1.2233] 0.3 soil
## 247.2 -1.0318 [-1.0322; -1.0313] 0.3 soil
## 247.3 -1.6740 [-1.6760; -1.6720] 0.3 soil
## 247.3 -1.6466 [-1.6485; -1.6447] 0.3 soil
## 247.3 -1.6740 [-1.6785; -1.6694] 0.3 soil
## 247.3 -1.7610 [-1.7664; -1.7556] 0.3 soil
## 247.3 -1.4733 [-1.4737; -1.4730] 0.3 soil
## 247.3 -1.5686 [-1.5690; -1.5682] 0.3 soil
## 247.3 -1.4073 [-1.4085; -1.4061] 0.3 soil
## 247.3 -1.4733 [-1.4737; -1.4730] 0.3 soil
## 247.3 -1.2872 [-1.2875; -1.2870] 0.3 soil
## 247.3 -1.0380 [-1.0381; -1.0378] 0.3 soil
## 247.3 -0.7036 [-0.7037; -0.7035] 0.3 soil
## 247.4 -1.6850 [-1.6851; -1.6850] 0.3 soil
## 247.4 -1.6333 [-1.6334; -1.6333] 0.3 soil
## 247.4 -1.3151 [-1.3151; -1.3150] 0.3 soil
## 247.4 -1.4102 [-1.4102; -1.4102] 0.3 soil
## 247.4 -1.2006 [-1.2083; -1.1930] 0.3 soil
## 247.4 -1.3151 [-1.3175; -1.3126] 0.3 soil
## 247.4 -0.8602 [-0.8604; -0.8599] 0.3 soil
## 247.4 -1.3151 [-1.3157; -1.3144] 0.3 soil
## 247.4 -1.0737 [-1.0746; -1.0729] 0.3 soil
## 247.4 -0.8224 [-0.8225; -0.8224] 0.3 soil
## 247.4 -0.6371 [-0.6371; -0.6371] 0.3 soil
## 247.5 -0.8473 [-0.9332; -0.7614] 0.3 soil
## 247.5 -0.5596 [-0.6138; -0.5055] 0.3 soil
## 247.5 -0.3365 [-0.3501; -0.3229] 0.3 soil
## 247.5 -0.5596 [-0.6138; -0.5055] 0.3 soil
## 247.5 -0.5596 [-0.6138; -0.5055] 0.3 soil
## 247.5 -0.8473 [-1.1510; -0.5436] 0.2 soil
## 247.5 0.0000 [-0.0267; 0.0267] 0.3 soil
## 247.5 -0.5596 [-0.5734; -0.5459] 0.3 soil
## 247.5 -0.8473 [-0.9332; -0.7614] 0.3 soil
## 247.5 -1.2528 [-1.4294; -1.0761] 0.3 soil
## 247.5 -0.8473 [-0.9332; -0.7614] 0.3 soil
## 247.6 -0.7613 [-0.7769; -0.7458] 0.3 soil
## 247.6 -0.3912 [-0.3915; -0.3909] 0.3 soil
## 247.6 -0.5032 [-0.5041; -0.5023] 0.3 soil
## 247.6 -0.4891 [-0.4907; -0.4875] 0.3 soil
## 247.6 -0.3497 [-0.3515; -0.3480] 0.3 soil
## 247.6 -0.1546 [-0.2180; -0.0912] 0.3 soil
## 247.6 -0.3786 [-0.4228; -0.3344] 0.3 soil
## 247.6 -0.6511 [-0.6513; -0.6509] 0.3 soil
## 247.6 -0.4083 [-0.4289; -0.3877] 0.3 soil
## 247.6 -0.2906 [-0.2908; -0.2903] 0.3 soil
## 247.6 -0.2791 [-0.2951; -0.2632] 0.3 soil
## 247.7 -0.6046 [-0.6046; -0.6045] 0.3 soil
## 247.7 -0.3781 [-0.3782; -0.3780] 0.3 soil
## 247.7 -0.7309 [-0.7319; -0.7299] 0.3 soil
## 247.7 -0.6749 [-0.6751; -0.6746] 0.3 soil
## 247.7 -0.6392 [-0.6394; -0.6389] 0.3 soil
## 247.7 -0.5878 [-0.5879; -0.5878] 0.3 soil
## 247.7 -0.5078 [-0.5092; -0.5063] 0.3 soil
## 247.7 -0.5713 [-0.5714; -0.5712] 0.3 soil
## 247.7 -0.6217 [-0.6218; -0.6217] 0.3 soil
## 247.7 -0.4337 [-0.4345; -0.4328] 0.3 soil
## 247.7 -0.1935 [-0.1936; -0.1934] 0.3 soil
## 247.8 0.0302 [ 0.0302; 0.0302] 0.3 soil
## 247.8 0.0129 [ 0.0129; 0.0129] 0.3 soil
## 247.8 0.0082 [ 0.0082; 0.0082] 0.3 soil
## 247.8 0.0233 [ 0.0233; 0.0233] 0.3 soil
## 247.8 0.0164 [ 0.0164; 0.0164] 0.3 soil
## 247.8 0.0129 [ 0.0129; 0.0129] 0.3 soil
## 247.8 0.0129 [ 0.0129; 0.0129] 0.3 soil
## 247.8 0.0129 [ 0.0129; 0.0129] 0.3 soil
## 247.8 0.0129 [ 0.0129; 0.0129] 0.3 soil
## 247.8 -0.0203 [-0.0203; -0.0202] 0.3 soil
## 247.8 -0.0059 [-0.0059; -0.0059] 0.3 soil
## 247.9 -1.7564 [-1.7565; -1.7564] 0.3 soil
## 247.9 -1.5672 [-1.5672; -1.5671] 0.3 soil
## 247.9 -1.6387 [-1.6387; -1.6386] 0.3 soil
## 247.9 -1.2125 [-1.2140; -1.2109] 0.3 soil
## 247.9 -1.3510 [-1.3510; -1.3509] 0.3 soil
## 247.9 -1.5333 [-1.5333; -1.5333] 0.3 soil
## 247.9 -0.9455 [-0.9475; -0.9434] 0.3 soil
## 247.9 -1.5333 [-1.5398; -1.5267] 0.3 soil
## 247.9 -1.1968 [-1.1968; -1.1968] 0.3 soil
## 247.9 -0.9455 [-0.9455; -0.9455] 0.3 soil
## 247.9 -0.8401 [-0.8402; -0.8401] 0.3 soil
## 255.1 0.3930 [ 0.3796; 0.4065] 0.3 plants
## 255.2 0.6107 [ 0.6009; 0.6205] 0.3 plants
## 255.3 0.3556 [ 0.3542; 0.3569] 0.3 plants
## 255.4 0.4290 [ 0.4282; 0.4298] 0.3 plants
## 255.5 0.9860 [ 0.9484; 1.0235] 0.3 plants
## 256.1 -0.4259 [-0.4498; -0.4020] 0.3 soil
## 256.1 -0.0676 [-0.0813; -0.0538] 0.3 soil
## 256.2 0.1888 [ 0.1835; 0.1941] 0.3 soil
## 256.2 0.0704 [ 0.0622; 0.0786] 0.3 soil
## 256.3 0.6531 [ 0.6241; 0.6822] 0.3 soil
## 256.3 0.1137 [ 0.0955; 0.1319] 0.3 soil
## 263.1 -0.5015 [-0.5142; -0.4888] 0.3 habitat
## 263.2 0.2503 [ 0.2404; 0.2601] 0.3 habitat
## 263.3 0.2096 [ 0.1986; 0.2206] 0.3 habitat
## 263.4 0.2178 [ 0.1827; 0.2528] 0.3 habitat
## 263.5 0.5900 [ 0.5224; 0.6575] 0.3 habitat
## 263.6 0.1460 [ 0.1256; 0.1663] 0.3 habitat
## 263.7 0.4443 [ 0.3364; 0.5521] 0.3 habitat
## 263.8 -0.0877 [-0.0877; -0.0877] 0.3 habitat
## 263.9 -0.0694 [-0.0694; -0.0694] 0.3 habitat
## 263.101 0.7529 [ 0.7529; 0.7529] 0.3 habitat
## 263.11 0.8505 [ 0.8505; 0.8505] 0.3 habitat
## 263.12 0.2636 [ 0.2636; 0.2636] 0.3 habitat
## 263.13 0.4229 [ 0.4229; 0.4229] 0.3 habitat
## 263.14 0.5410 [ 0.5410; 0.5410] 0.3 habitat
## 263.15 0.8473 [ 0.8473; 0.8473] 0.3 habitat
## 263.16 0.6910 [ 0.6910; 0.6910] 0.3 habitat
## 263.17 0.1144 [ 0.1144; 0.1144] 0.3 habitat
## 263.18 0.5340 [ 0.5340; 0.5340] 0.3 habitat
## 263.19 0.2467 [ 0.2467; 0.2467] 0.3 habitat
## 263.201 0.3095 [ 0.3095; 0.3095] 0.3 habitat
##
## Number of studies combined: k = 358
##
## 95%-CI z p-value
## Random effects model -0.3413 [-0.3753; -0.3073] -19.70 < 0.0001
##
## Quantifying heterogeneity:
## tau^2 = 0.1047; H = 3410410951.75 [3410410950.14; 3410410953.36]; I^2 = 100.0% [100.0%; 100.0%]
##
## Test of heterogeneity:
## Q d.f. p-value
## 4152232320954931871744.00 357 0
##
## Results for subgroups (random effects model):
## k 95%-CI Q
## outcome = habitat 104 0.1605 [ 0.0964; 0.2246] 4152172019980636258304.00
## outcome = plants 50 0.4438 [ 0.0345; 0.8532] 33314950066906688.00
## outcome = soil 204 -0.7583 [-0.8196; -0.6970] 14453104123321616.00
## tau^2 I^2
## outcome = habitat 0.1047 100.0%
## outcome = plants 2.1620 100.0%
## outcome = soil 0.1990 100.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 425.72 2 < 0.0001
##
## Details on meta-analytical method:
## - Inverse variance method
## - DerSimonian-Laird estimator for tau^2
#metabias(m)
mr5 <- metareg(m5, ~ aridity.index*exp.length)
mr5
##
## Mixed-Effects Model (k = 358; tau^2 estimator: DL)
##
## tau^2 (estimated amount of residual heterogeneity): 0.1047 (SE = 0.0876)
## tau (square root of estimated tau^2 value): 0.3236
## I^2 (residual heterogeneity / unaccounted variability): 100.00%
## H^2 (unaccounted variability / sampling variability): 11729120705162215424.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 354) = 4152108729627424325632.0000, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 882.1252, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1386 0.0636 2.1770 0.0295 0.0138
## aridity.index 0.0042 0.0029 1.4244 0.1543 -0.0016
## exp.length 0.0106 0.0015 6.8767 <.0001 0.0076
## aridity.index:exp.length -0.0005 0.0001 -8.1677 <.0001 -0.0006
## ci.ub
## intrcpt 0.2633 *
## aridity.index 0.0099
## exp.length 0.0136 ***
## aridity.index:exp.length -0.0004 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(mr5)
#detach(package:meta, unload = TRUE)
#library(metafor)
#must use dev version of metafor (https://wviechtb.github.io/metafor/#installation)
#data<-escalc(measure="ROM",m1i=mean.t,m2i=mean.c,sd1i=sd.t,sd2i=sd.c,n1i=n.t,n2i=n.c, #data=mydata,var.names=c("LRR","LRR_var"),digits=4)
#data <- data %>%
# filter(!is.na(LRR)) %>%
# filter(!is.na(LRR_var)) %>%
# filter(!is.na(n.t)) %>%
# filter(!is.na(p)) %>%
# filter(!is.na(intervention)) %>%
# filter(is.finite(lrr)) %>%
# filter(!is.na(exp.length)) %>%
# filter(!is.na(aridity.index))
#mod.1 <- rma(yi=lrr, vi=var.es, data = mdata.all)
#summary(mod.1)
#exp(0.0899) #estimate
#mod.2 <- rma(lrr, var.es, mods= ~paradigm+aridity.index+exp.length -1, data = mdata.all)
#summary(mod.2)
#Evaluating the influence of moderators on the heterogeneity
#interventions
#mod.3 <- rma(lrr, var.es, slab= ID, mods= ~intervention+aridity.index+exp.length -1, data = mdata.all, subset = paradigm == "active")
#summary(mod.3)
#forest(mod.3, slab= "study.ID")
#mod.4 <- rma(lrr, var.es, slab= ID, mods= ~intervention+aridity.index+exp.length -1, data = mdata.all, subset = paradigm == "passive")
#summary(mod.4) #aridity.index and exp.length non significants
#mod.4b <- rma(lrr, var.es, slab= ID, mods= ~intervention -1, data = mdata.all, subset = paradigm == "passive")
#summary(mod.4b)
#outcomes
#mod.5 <- rma(lrr, var.es, slab= ID, mods= ~outcome+aridity.index+exp.length -1, data = mdata.all, subset = paradigm == "active")
#summary(mod.5)
#mod.6 <- rma(lrr, var.es, slab= ID, mods= ~outcome+aridity.index+exp.length -1, data = mdata.all, subset = paradigm == "passive")
#summary(mod.6) #aridity.index non significant
#mod.6b <- rma(lrr, var.es, slab= ID, mods= ~outcome+exp.length -1, data = mdata.all, subset = paradigm == "passive")
#summary(mod.6b)